Business Intelligence Dashboards present frequencies, percentages and averages in one convenient location. In this example, there are five average “Wait Time” scores compared for meaningful differences. Each average Wait Time score represents a single week of calls to a Government Department. Total number of calls, or “n” appears in the *Total** Calls *column. It is important to know “n”, in this case call volumes, when interpreting averages. Calculated Wait Time averages appear in

*the*

*Average Wait Time*column (second table). There is no need to guess from the bar chart what the exact values are.

This example is from a Government Website (http://www.tpsgc-pwgsc.gc.ca/pension/qenrcm-hdwdtm-eng.html). It’s a great resource with lots of interesting analytics on key performance indicators.

**General Questions From an Operations Manager:**

Is the Wait Time average downward trend a good thing? Are Wait Time averages really different for each week? Is there a significant improvement in Wait Times over the five-week period? What does the increase in the last week suggest? Should something be done to make improvements in our service delivery since it looks like Wait Times are going back up? Do we hire or fire call centre staff?

**Research Questions From the Analytics Manager:**

Are the differences in the average Wait Times statistically meaningful? If they are, what month(s) show the greatest improvement? Depending on the statistical results, are the changes in average Wait Time practically meaningful?

**Hypothesis From the Statistician:**

*Ho: There is No statistical difference in average Wait Time for the five weeks.*

*Ha: There IS a statistical difference in average Wait Time for the five weeks. *

**Statistical Test:**

The One-way Analysis of Variance (ANOVA) is the appropriate statistical test to use when comparing the differences between three or more averages.

**Results & Interpretation:**

In this example, the ANOVA will determine if the observed differences in average Wait Time is statistically different. The chart suggests a dramatic downward trend with large differences. The ANOVA test will help managers make a practical interpretation of the apparent trend. For example, if the downward trend is statistically significant, managers may check customer satisfaction to confirm if a decrease in Wait Time by 41 seconds really improves service? If the trend is statistically significant, managers may also want to reward staff for their excellent performance.

More about the ANOVA test: http://www.csse.monash.edu.au/~smarkham/resources/anova.htm

ANOVA test in SPSS: https://statistics.laerd.com/spss-tutorials/one-way-anova-using-spss-statistics.php